{"id":"W4312731466","doi":"10.30932/1992-3252-2017-15-3-29","title":"MOSCOW HAS GOT ONE OF GLOBAL PUBLIC TRANSPORT AWARDS 2017","year":2017,"lang":"en","type":"article","venue":"World of Transport and Transportation","topic":"Arctic and Russian Policy Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Political science; Public administration; Library science; Media studies; Sociology; Computer science; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004295776,0.0001890241,0.0004912537,0.0000798805,0.001126767,0.00002276332,0.000355232,0.0001129554,0.0001121625],"category_scores_gemma":[0.0000149252,0.0001867713,0.0001941338,0.0001947956,0.001879127,0.0005041595,0.00000243566,0.0001113399,0.000001635361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002828162,"about_ca_system_score_gemma":0.000293089,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01883877,"about_ca_topic_score_gemma":0.2704293,"domain_scores_codex":[0.9982289,0.00001957757,0.0005720961,0.0002914731,0.0005307305,0.0003572165],"domain_scores_gemma":[0.9988846,0.00002638099,0.0004390263,0.0003133829,0.0001514177,0.0001852475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008878682,0.0001486401,0.8907958,0.0001494217,0.0001365324,0.000009891429,0.008425073,0.000003137021,0.00004246605,0.09789439,0.0001221999,0.002183681],"study_design_scores_gemma":[0.0007199566,0.00004615038,0.981715,0.00009841603,0.00022335,2.453754e-7,0.0007798572,0.00000132152,0.0001347767,0.003583638,0.01251088,0.0001864336],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9266975,0.0005555683,0.0009327907,0.01691405,0.0003031124,0.0004345231,0.0004946886,0.00006346196,0.05360428],"genre_scores_gemma":[0.9976257,0.0006800939,0.0006007255,0.00004032791,0.0001039604,0.00001213426,0.00006124983,0.00001214546,0.0008636729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2515905,"threshold_uncertainty_score":0.9876949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06314065978067161,"score_gpt":0.3259517915177026,"score_spread":0.262811131737031,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}